Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, People's Republic of China.
Stat Med. 2011 Aug 15;30(18):2265-77. doi: 10.1002/sim.4260. Epub 2011 May 18.
Recurrent event data arise frequently from medical research. Examples include repeated infections, recurrence of tumors, relapse of leukemia, repeated hospitalizations, recurrence of symptoms of a disease, and so on. In the analysis of recurrent event data, the proportional rates model assumes that the regression coefficients are time invariant. In reality, however, these parameters may vary over time, and the temporal covariate effects on the event process are of great interest. In this article, we formulate a class of semiparametric marginal rates models, which incorporate a mixture of time-varying and time-independent parameters, to analyze recurrent event data. For statistical inference on model parameters, an estimation procedure is developed and asymptotic properties of the proposed estimators are established. In addition, we develop tests for investigating whether or not covariate effects vary with time. The finite-sample behaviors of the proposed methods are examined in simulation studies. An example of application of the proposed methodology is illustrated on a set of data from a clinic study on chronic granulomatous disease.
经常会从医学研究中产生重复事件数据。例如，反复感染、肿瘤复发、白血病复发、多次住院、疾病症状再次出现等。在重复事件数据的分析中，比例速率模型假设回归系数是时间不变的。然而，实际上，这些参数可能随时间变化，而事件过程的时间协变量效应非常重要。本文提出了一类半参数边缘速率模型，该模型将随时间变化和时间独立的参数混合在一起，以分析重复事件数据。对于模型参数的统计推断，我们开发了一种估计程序，并建立了所提出估计量的渐近性质。此外，我们还开发了检验协变量效应是否随时间变化的检验方法。通过模拟研究检验了所提出方法的有限样本行为。通过慢性肉芽肿病临床研究数据的实例说明了所提出方法的应用。
